Abstract
This paper presents a method for the forecasting of the
voltage and the frequency at the point of connection between
a battery energy storage system installed at The University of
Manchester and the local low voltage distribution grid. The
techniques are to be used in a real-time controller for optimal
management of the storage system. The forecasters developed
in this study use an Artificial Neural Network (ANN)-based
technique and can predict the grid quantities with two
different time widows: one second and one minute ahead. The
developed ANNs have been implemented in a dSPACE based
real-time controller and all forecasters show very good
performance, with correlations coefficients greater than 0.85,
and Mean Absolute Percentage Errors of less than 0.2 %.
voltage and the frequency at the point of connection between
a battery energy storage system installed at The University of
Manchester and the local low voltage distribution grid. The
techniques are to be used in a real-time controller for optimal
management of the storage system. The forecasters developed
in this study use an Artificial Neural Network (ANN)-based
technique and can predict the grid quantities with two
different time widows: one second and one minute ahead. The
developed ANNs have been implemented in a dSPACE based
real-time controller and all forecasters show very good
performance, with correlations coefficients greater than 0.85,
and Mean Absolute Percentage Errors of less than 0.2 %.
| Original language | English |
|---|---|
| Publication status | Published - 2018 |
| Event | IET International Conference on Power Electronics, Machines and Drives (PEMD) - Liverpool, United Kingdom Duration: 17 Apr 2018 → 19 Jul 2018 https://events.theiet.org/pemd/about.cfm |
Conference
| Conference | IET International Conference on Power Electronics, Machines and Drives (PEMD) |
|---|---|
| Country/Territory | United Kingdom |
| Period | 17/04/18 → 19/07/18 |
| Internet address |